Comments on Mohr and Henderson's path consistency algorithm
Artificial Intelligence
Temporal logic for real time systems
Temporal logic for real time systems
Constraint propagation algorithms for temporal reasoning: a revised report
Readings in qualitative reasoning about physical systems
Artificial Intelligence - Special issue on knowledge representation
Reasoning about qualitative temporal information
Artificial Intelligence - Special volume on constraint-based reasoning
Temporal databases: theory, design, and implementation
Temporal databases: theory, design, and implementation
Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra
Journal of the ACM (JACM)
Scheduling Jobs with Temporal Distance Constraints
SIAM Journal on Computing
Processing disjunctions in temporal constraint networks
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Maintaining knowledge about temporal intervals
Communications of the ACM
Temporal Constraints: A Survey
Constraints
Effective Scheduling of Tasks under Weak Temporal Interval Constraints
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
On-Line Algorithms for Networks of Temporal Constraints
WG '97 Proceedings of the 23rd International Workshop on Graph-Theoretic Concepts in Computer Science
Verification of Concurrent Programs: Temporal Proof Principles
Logic of Programs, Workshop
| Hi-index | 0.00 |
We consider a semi-dynamic setting for the Temporal Constraint Satisfaction Problem (TCSP), where we are requested to maintain the path-consistency of a network under a sequence of insertions of new (further) constraints between pairs of variables. We show how to maintain the path-consistency in O(nR3) amortized time on a sequence of Θ(n2) insertions, where n is the number of vertices of the network and R is its range, defined as the maximum size of the minimum interval containing all the intervals of a single constraint.Furthermore we extend our algorithms to deal with more general temporal networks where variables can be points and/or intervals and constraints can also be defined on pairs of different kinds of variables. For such cases our algorithms maintain their performance. Finally, we adapt our algorithms to also maintain the arc-consistency of such generalized networks in O(R) amortized time for Θ(n2) insertions.